High-throughput signal detection of hepatotoxic drug-drug interactions in hospitalized elderly patients: an NLP-driven pharmacovigilance study.

BACKGROUND

Elderly patients are highly susceptible to drug-drug interaction (DDI)-induced liver injury, yet comprehensive real-world evidence remains scarce. This study aimed to leverage a natural language processing (NLP) model to efficiently identify hepatotoxicity-associated DDI signals in this population.

METHODS

This retrospective study analyzed electronic health records from 109,263 elderly inpatients. An integrated approach combining laboratory thresholds and NLP was utilized to precisely identify liver injury cases. High-throughput case-control analyses were conducted using additive and multiplicative interaction models to screen for DDI signals. To minimize false positives, time-dependent causality inference and sensitivity analyses were performed, followed by validation using in vivo animal experiments.

RESULTS

Among 3,227 drug combinations evaluated, 111 signals were identified, 58 of which demonstrated consistent time-dependent risk trends. Notably, among the top 20 DDI signals with the strongest associations, a marked risk elevation was observed for cardiovascular medications such as aspirin (additive: 1.16; multiplicative: 2.49), clopidogrel (additive: 1.12; multiplicative: 2.12), and atorvastatin (additive: 0.52; multiplicative: 1.88) when co-administered with the antimicrobial agent piperacillin/tazobactam. Animal experiments and sensitivity analyses further corroborated these findings.

CONCLUSION

We established a robust NLP-based framework to efficiently evaluate DDI-induced hepatotoxicity in elderly inpatients. By identifying novel high-risk combinations, this study provides critical insights for optimizing pharmacotherapy and highlights the potential of artificial intelligence technologies in real-world pharmacovigilance study.